Analytical uncertainties and model output Anders Grimvall 1, Claudia von Brömssen 2 and Göran Lindström 3 1) Swedish Institute for the Marine Environment 2) Swedish Univerity of Agricultural Sciences 3) Swedish Meteorological and Hydrological Institute
Monitoring and modelling riverine loads of substances Measured water quality and discharge Process-based catchment model (e.g. HYPE) High quality monitoring is a prerequisite for high quality modelling!
Monitoring and modelling river water quality Process-based catchment models (e.g. HYPE) Measured water quality and discharge Can process-based models help to reduce the uncertainty of measured data?
Principle premise Constant landuse and point emissions Observed temperature and rainfall Process-based catchment model (e.g. HYPE) Weatherdriven temporal variation The impact of human interventions in the catchment will emerge more clearly If weather-driven fluctuations are removed from observed data
Adjustment methods Simplistic method Substract calculated weather-driven fluctuations from observed values Slightly more advanced method Regress observed values on modelled values and compute residuals. Focus on the feasibility to predict the irregular components (anomalies) of time series
HYPE HYdrological Predictions for the Environment The soil is modelled as several layers which may have different thickness for each soil class
S-HYPE simulations S-HYPE is a HYPE setup that covers the whole of Sweden Sweden was divided into 37786 sub-basins The anthropogenic forcing was kept fixed to the conditions prevailing in 2005 The physical forcing consisted of time series of meteorological data from 1992 to 2010. We used monthly mean model outputs for water discharge and nitrogen and phosphorus concentrations at major river mouths
River Sampling site Observational data Sampling period Sea area Monthly sampling for water quality. Daily water discharge. River Sampling site Sampling period Sea area Norrström Stockholm Centralbron 1996 2010 Baltic Proper Örekilsälven Munkedal 1992 2010 Skagerrak Norrström Stockholm Norrström 1992 2002 Baltic Proper Enningdalsälven N. Bullaren 1992 2010 Skagerrak Nyköpingsån Spånga 1992 2010 Baltic Proper Forsmarksån Johannisfors 1992 2010 Gulf of Bothnia Motala ström Norrköping 1992 2010 Baltic Proper Dalälven Älvkarleby 1992 2010 Gulf of Bothnia Ljungbyån Ljungbyholm 1992 2010 Baltic Proper Gavleån Gävle 1992 2010 Gulf of Bothnia Alsterån Getebro 1992 2010 Baltic Proper Ljusnan Ljusne Strömmar 1992 2010 Gulf of Bothnia Emån Emsfors 1992 2010 Baltic Proper Delångersån Iggesund 1992 2010 Gulf of Bothnia Botorpström Brunnsö 1992 2010 Baltic Proper Ljungan Skallböleforsen 1992 2010 Gulf of Bothnia Gothemsån Hörsne 1992 2010 Baltic Proper Indalsälven Bergeforsen 1992 2010 Gulf of Bothnia Mörrumsån Mörrum 1992 2010 Baltic Proper Ångermanälven Sollefteå 1992 2010 Gulf of Bothnia Lyckebyån Lyckeby 1992 2010 Baltic Proper Gide älv Gideåbacka 1992 2010 Gulf of Bothnia Skivarpsån Skivarp 1992 2010 Baltic Proper Lögde älv Lögdeå 1992 2010 Gulf of Bothnia Kävlingeån Högsmölla 1996 2010 Baltic Proper Öre älv Torrböle 1992 2010 Gulf of Bothnia Helgeån Hammarsjön 1992 2010 Baltic Proper Ume älv Stornorrfors 1992 2010 Gulf of Bothnia Råån Helsingborg 1992 2010 Öresund Rickleån Rickleån outflow 1992 2010 Gulf of Bothnia Rönneå Klippan 1992 2010 Kattegat Skellefte älv Kvistforsen 1992 2010 Gulf of Bothnia Smedjeån V. Mellby 1992 2010 Kattegat Pite älv Bölebyn 1992 2010 Gulf of Bothnia Lagan Laholm 1992 2010 Kattegat Alterälven Norrfjärden 1992 2010 Gulf of Bothnia Nissan Halmstad 1992 2010 Kattegat Lule älv Luleå 1992 2010 Gulf of Bothnia Ätran Falkenberg 1992 2010 Kattegat Kalix älv Karlsborg 1992 2010 Gulf of Bothnia Viskan Åsbro 1992 2010 Kattegat Töre älv Bölträsket inflow 1992 2010 Gulf of Bothnia Göta älv Alelyckan 1992 2010 Kattegat Torne älv Mattila 1992 2010 Gulf of Bothnia Göta älv Trollhättan 1992 2010 Kattegat Råne älv Niemisel 1992 2010 Gulf of Bothnia Bäveån Uddevalla 1992 2010 Skagerrak
S-HYPE performance: annual means
S-HYPE performance: seasonal components for total N
S-HYPE performance: seasonal components for total P
Prediction of anomalies, i.e the irregular components of a time series Which is the best predictor of observed anomalies? S-HYPE anomalies or water discharge anomalies?
S-HYPE performance: monthly anomalies for total N Measured anomalies are sometimes slightly better correlated to modelled anomalies than to water discharge anomalies
S-HYPE performance: annual anomalies for total N Measured anomalies are sometimes better correlated to modelled anomalies than to water discharge anomalies
S-HYPE performance: annual anomalies for total P With few exceptions, measured anomalies are poorly correlated to both modelled anomalies and water discharge anomalies
Weather-normalized annual anomalies of total N Red solid line: modelled anomalies Blue dashed line: observed anomalies
Mann-Kendall tests for monotone trends: unadjusted and weather-normalized total N data Annual measured anomaly (Total N: mg/l) Annual regression residual (Total N: mg/l) River/Sea area p-value (twosided) Significance code Slope (change/year) p-value (twosided) Significance code Slope (change/year) Mean total N (mg/l) Enningdalsälv 0.1154-4.4 0.2208-2.9 623 Bäveån 0.0543-8.3 0.2483-2.6 939 Örekilsälven 0.0029 - - -11.8 0.3449-4.1 1013 Skagerrak 0.0116 - -7.4 0.1695-3.0 Göta älv, Trollhättan 0.0001 - - - -8.2 0.3103-1.8 793 Göta älv, Alelyckan 0.0071 - - -8.3 0.8065-0.2 833 Lagan 0.7004 0.5 0.9721 0.1 908 Nissan 0.8611 1.3 0.5520 1.9 1029 Ätran 0.0107 - -15.4 0.2781-4.3 1183 Viskan 0.0637-8.6 0.3818-4.2 1281 Rönneå 0.0037 - - -30.3 0.0744-15.2 2316 Smedjeån 0.0001 - - - -89.5 0.0046 - - -65.6 4438 Kattegat 0.0023 - - -9.7 0.1099-2.6 Kävlingeån 0.0001 - - - -168.6 0.0001 - - - -168.3 4215 Råån 0.0000 - - - -272.2 0.0000 - - - -260.6 6714 Öresund 0.0000 - - - -208.6 0.0000 - - - -204.2
Mann-Kendall tests for monotone trends: unadjusted and weather-normalized total N data Annual measured anomaly (Total N: mg/l) Annual regression residual (Total N: mg/l) River/Sea area p-value (twosided) Significance code Slope (change/year) p-value (twosided) Significance code Slope (change/year) Mean total N (mg/l) Norrström, Centralbron 0.9605-0.6 0.9605 0.2 672 Alsterån 0.3818 3.3 0.4210 3.5 700 Norrström, Norrström 0.0240 + 24.5 0.1857 13.6 726 Botorpström 0.0865 6.0 0.0230 + 7.7 794 Mörrumsån 0.0191 + 6.8 0.0230 + 6.6 835 Motala ström 0.9164-0.6 0.8065 1.0 869 Emån 0.0461 + 7.4 0.1001 5.4 945 Lyckebyån 0.0637 7.0 0.1515 5.2 996 Nyköpingsån 0.3103-4.3 0.4210-2.5 1046 Helgeån 0.4625-7.0 0.6492-2.5 1663 Ljungbyån 0.0107 - -43.8 0.0230 - -43.2 1875 Gothemsån 0.1325-39.4 0.0543-45.7 3641 Skivarpsån 0.0037 - - -136.1 0.0005 - - - -126.2 5421 Baltic Proper 0.8746 0.6 0.9480 0.2
Mann-Kendall tests for monotone trends: observed total-p anomalies and residuals after regressing on S-HYPE anomalies Annual measured anomaly (Total P: mg/l) Annual regression residual (Total P: mg/l) River/Sea area p-value (twosided) Significance code Slope (change/year) p-value (twosided) Significance code Slope (change/year) Mean total P (mg/l) Enningdalsälv 0.0540-0.08 0.1724-0.06 11.6 Bäveån 0.2340-0.23 0.1724-0.19 35.9 Örekilsälven 0.0328 - -0.46 0.0191 - -0.49 37.4 Skagerrak 0.0128 - -0.17 0.0194 - -0.17 Göta älv, Trollhättan 0.0106 - -0.19 0.0865-0.12 12.4 Göta älv, Alelyckan 0.3627-0.10 0.5520-0.05 18.8 Lagan 0.2208-0.17 0.2483-0.15 20.2 Ätran 0.1614-0.28 0.0543-0.28 21.5 Nissan 0.0041 - - -0.36 0.0461 - -0.23 25.1 Viskan 0.0003 - - - -1.07 0.0007 - - - -1.07 36.9 Rönneå 0.9721 0.03 0.7004 0.21 51.8 Smedjeån 0.7794 0.20 0.3103 0.29 60.6 Kattegat 0.0172 - -0.23 0.0736-0.17 Kävlingeån 0.1815-0.69 0.6560-0.26 81.9 Råån 0.0002 - - - -3.46 0.0000 - - - -3.19 118.3 Öresund 0.0008 - - - -2.70 0.0003 - - - -2.49 Alsterån 0.4833-0.02 0.3449-0.05 14.6 Emån 0.6740 0.05 0.4625 0.06 18.4 Botorpström 0.0251 + 0.32 0.1724 0.14 19.0 Mörrumsån 0.0037 + + 0.37 0.0018 + + 0.39 24.4 Lyckebyån 0.7526-0.01 0.9721-0.01 25.7 Ljungbyån 0.3103-0.28 0.3103-0.24 25.7 Norrström, Centralbron 0.0197 - -0.30 0.0200 - -0.22 28.8 Motala ström 0.0143 - -0.50 0.0130 - -0.51 37.1 Helgeån 0.4210 0.27 0.7004 0.19 37.1 Norrström, Norrström 0.5858-0.58 0.3115-0.69 37.9 Nyköpingsån 0.0390 - -0.48 0.1001-0.39 43.3 Gothemsån 0.0637-0.96 0.0543-1.04 67.1 Skivarpsån 0.0158 - -1.51 0.0158 - -1.23 136.4 Baltic Proper 0.2107-0.08 0.1618-0.09
Mann-Kendall tests for monotone trends: observed total-p anomalies and residuals after regressing on S-HYPE anomalies Annual measured anomaly (Total P: mg/l) Annual regression residual (Total P: mg/l) River/Sea area p-value (twosided) Significance code Slope (change/year) p-value (twosided) Significance code Slope (change/year) Mean total P (mg/l) Indalsälven 0.0005 - - - -0.22 0.0011 - - -0.23 6.3 Skellefte älv 0.0000 - - - -0.25 0.0000 - - - -0.25 7.2 Lule älv 0.0000 - - - -0.25 0.0001 - - - -0.25 7.4 Ångermanälven 0.0013 - - -0.21 0.0009 - - - -0.22 8.1 Ume älv 0.0015 - - -0.24 0.0015 - - -0.23 8.5 Delångersån 0.0071 - - -0.19 0.0107 - -0.16 9.6 Ljungan 0.0029 - - -0.41 0.0018 - - -0.45 10.6 Ljusnan 0.0275 - -0.27 0.0230 - -0.25 11.5 Pite älv 0.7794-0.03 0.8611 0.01 11.9 Råne älv 0.0032 - - -0.31 0.0107 - -0.24 14.3 Gide älv 0.0004 - - - -0.41 0.0007 - - - -0.42 14.5 Dalälven 0.0045 - - -0.26 0.0023 - - -0.25 14.9 Kalix älv 0.0045 - - -0.33 0.4210-0.08 15.6 Rickleån 0.0063 - - -0.23 0.0071 - - -0.24 16.0 Öre älv 0.3449-0.17 0.9721-0.01 18.4 Torne älv 0.2936-0.17 0.7529 0.07 18.6 Forsmarksån 0.7000-0.04 0.8065-0.02 19.0 Lögde älv 0.0637-0.45 0.0865-0.41 20.2 Gavleån 0.1235-0.18 0.1154-0.17 26.7 Alterälven 0.2781-0.15 0.2483-0.22 27.5 Töre älv 0.1001-0.39 0.1154-0.35 31.3 Gulf of Bothnia 0.0004 - - - -0.24 0.0007 - - - -0.22
Level shifts in particulate and soluble P: in low total P rivers
Statistical break-point analysis of particulate P anomalies Mean concentration of total phosphorus (mg/l) Level shifts in standardized concentration anomalies Magnitude Two-sided p-value Level shifts in standardized regression residuals Magnitude Two-sided p-value 0 10 0.2143 0.0103 0.2586 0.0017 10 20 0.2085 0.0021 0.2143 0.0013 20 30 0.1482 0.0413 0.1764 0.0124 30 40 0.2021 0.0096 0.2511 0.0016 > 40 0.0411 0.6134 0.0477 0.5319 0 100 0.1684 0.0039 0.1908 0.0009 Mixed linear model with quadratic mean function and a level shift between 2001 and 2002
Conclusions Joint analysis of model outputs and measured total N and total P has: Helped to attribute trends in total N to measures in agriculture Helped to reveal a break-point in particulate P Strengthened the need for better feedback from modelling yo monitoring Raised the question if flow adjustment of riverine loads can be substituted for procedures involving joint analysis of measured data and model outputss
Reference Grimvall, A., von Brömssen, C. and Lindström, G. (2014) Using process-based models to filter out natural variability in observed concentrations of nitrogen and phosphorus in river water. Environmental Monitoring and Assessment. 186:5135-5152.